Share Email Print

Proceedings Paper

New Results On State-Space And Input-Output Identification Of Non-Gaussian Processes Using Cumulants
Author(s): Georgios B. Giannakis; Ananthrarn Swami
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Closed form expressions and recursive equations relating the parameters of an ARMA model (which may be non-minimum phase, non-causal or may even contain all-pass factors) with the cumulants of its output, in response to excitation by a non-Gaussian i.i.d. process are derived. Based on these relationships, system identification and order determination algorithms are developed. The output noise may be colored Gaussian or i.i.d. non-Gaussian. When a state-space representation is adopted, the stochastic realization problem reduces to the balanced realization of an appropriate Hankel matrix formed by cumulant statistics. Using a Kronecker product formulation, an exact expression is presented for identifying state-space quantities when output cumulants are provided, or for computing output cumulants when the state-space triple is known. If a transfer function approach is employed, cumulant based recursions are proposed to reduce the AR parameter estimation problem to the solution of a system of linear equations. Closed form expressions and alternative formulations are given to cover the case of non-causal processes.

Paper Details

Date Published: 21 January 1988
PDF: 6 pages
Proc. SPIE 0826, Advanced Algorithms and Architectures for Signal Processing II, (21 January 1988); doi: 10.1117/12.942033
Show Author Affiliations
Georgios B. Giannakis, University of Virginia (United States)
Ananthrarn Swami, University of Southern California (United States)

Published in SPIE Proceedings Vol. 0826:
Advanced Algorithms and Architectures for Signal Processing II
Franklin T. Luk, Editor(s)

© SPIE. Terms of Use
Back to Top